A Schmitdy dolphin planning a week of recipes in a harbour market kitchen

UK Recipe Apps and Meal Planning in 2026: 1,432 Answers Compared

TL;DR

  • We tracked 55 UK buyer questions covering weeknight meals, batch cooking, healthy food, family budgets and recipe apps.
  • The category comparison draws on 1,432 answers collected from ChatGPT, Gemini and Google AI Overview on 19 August 2026.
  • Paprika led at 20.9% visibility, followed by Good Food at 15.7% and Mealime at 13.3%.
  • MOB reached 3.0% visibility overall and was strongest in ChatGPT.
  • Good Food, Reddit, YouTube and NHS sources shaped much of the answer set.

Recipe discovery and meal planning sound like the same job. They are not.

A recipe site helps with one meal. A planning tool has to carry that choice through a week, a shopping list, a budget, nutrition needs, leftovers and the time available to cook. That change in job explains why a large recipe library does not automatically lead recommendations for meal-planning apps.

We tracked 55 UK questions across five common cooking jobs and reviewed 1,432 category answers from ChatGPT, Gemini and Google AI Overview. Five direct brand-check questions were kept outside the category ranking, so the comparison uses the 50 non-branded buyer questions.

Which recipe and meal-planning brands lead?

Paprika led the measured set at 20.9% visibility and 35.4% share of voice. Good Food followed at 15.7% visibility and 33.4% share of voice. Mealime reached 13.3%.

MOB appeared in 3.0% of answers, ahead of Sorted Food at 1.5% in this tracked set. Deliciously Ella did not appear in the category comparison.

These brands solve different jobs. Good Food has a large UK recipe and editorial footprint. Paprika is a focused recipe manager. Mealime starts with a plan and turns it into a shopping list. MOB combines tested recipes, editorial voice and paid planning features.

The answer engines often flatten those differences. A useful comparison should separate finding a recipe, saving one, building a plan, shopping for it and meeting a nutrition or budget goal.

How does the ranking change by question?

MOB performed best on easy weeknight recipes and recipe apps. It was much weaker on meal planning and batch cooking, and it had no measured visibility on the high-protein or affordable family-cooking groups in the first pull.

That is a positioning problem more than a recipe-volume problem. The site already has thousands of recipes. Buyers and answer engines need clear paths that join those recipes into a practical result.

A strong weekly hub should answer:

  • what to cook on each day
  • how the recipes share ingredients
  • what can be prepared in advance
  • how the shopping list changes by household size
  • how much the basket should cost
  • where nutrition figures come from
  • how leftovers are reused safely

Without that layer, individual recipes compete one by one while planning apps own the workflow.

Which engine is easiest to win?

MOB reached 5.4% visibility in ChatGPT. Google AI Overview and Gemini were lower.

The difference is useful. ChatGPT already connects MOB with parts of the recipe and app category. The weaker engines need clearer entity links between the publisher, app, subscription, meal plans, chefs and recipes.

The brand name creates another challenge. "Mob" is a common word. Consistent organisation and software-application data, stable naming and repeated third-party descriptions help answer engines resolve which Mob a cooking question means.

What should buyers compare in a recipe app?

The right app depends on the bottleneck.

Choose a recipe manager when the problem is saving and organising recipes from many sites. Compare import quality, tags, folders, offline access and household sharing.

Choose a meal planner when the problem is deciding what to cook across a week. Compare plan flexibility, shopping-list quality, serving changes, dietary filters and how easily meals can move between days.

Choose a cooking publisher when tested recipes and clear technique matter most. Compare recipe testing, author credentials, update dates, nutrition methods and whether the free experience is usable.

Subscriptions need a separate check. Look at what is free, what sits behind the paywall, whether a plan exports, and what happens to saved recipes when the subscription ends.

Which sources shape the answers?

Good Food was the strongest measured domain, receiving 979 citations in the final source pull. Reddit received 525, YouTube 433 and NHS pages 257.

That mix shows how buyers make the decision. Editorial recipe pages provide the dish. Community discussions test whether an app fits real life. Video demonstrates the method. NHS material supports nutrition and food-safety claims.

App-store pages and supermarket content also matter because they answer product availability, reviews, ingredients and basket questions.

For a recipe brand, a sound source plan joins all four:

  1. tested recipe and planning pages on the owned site
  2. clear app and subscription facts
  3. independent comparisons using real weekly workflows
  4. community and video evidence that shows the plan being used

What will change the 2026 ranking?

Paprika, Good Food and Mealime lead for different reasons. Paprika is easy to classify as a tool. Good Food has category authority and UK reach. Mealime owns the plan-to-list workflow.

MOB has enough recipe depth to compete. Its opportunity is to make the planning system as visible and extractable as the recipes. High-protein plans, family-budget plans and batch-cooking systems are clear places to start because the first measured set showed almost no brand presence there.

The winner will not be the site with the most recipes. It will be the service that helps a buyer move from "what should I cook?" to a workable week with the least friction and the clearest proof.

Sources

If you want to measure the buyer questions in your category, book a 20-minute call with Marco: https://calendly.com/marco-ai-heroes/20min.

Frequently Asked Questions

Marco Lobo
Marco Lobo

Founder, Schmitdy

Marco builds AI search growth systems that turn prompts, sources, content, and agents into revenue.

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